System and Method for Tracking

ABSTRACT

Systems and methods are provided for tracking at least position and angular orientation. The system comprises a computing device in communication with at least two cameras, wherein each of the cameras are able to capture images of one or more light sources attached to an object. A receiver is in communication with the computing device, wherein the receiver is able to receive at least angular orientation data associated with the object. The computing device determines the object&#39;s position by comparing images of the light sources and generates an output comprising the position and angular orientation of the object.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation-In-Part of U.S. patent application Ser. No. 12/872,956 filed on Aug. 31, 2010, and titled “System and Method for Tracking”, the entire contents of which are incorporated herein by reference.

TECHNICAL FIELD

The following relates generally to tracking the position or movement, or both, of an object.

DESCRIPTION OF THE RELATED ART

Tracking an object can be difficult, especially when the object's movements are unpredictable and erratic. For example, accurately tracking the movement of a person or an animal can be difficult. Known tracking systems attempt to capture such movement using visual imaging systems. However, processing the images can be resource intensive and slow down the tracking response rate. Other known tracking systems that are able to quickly track movements tend to be inaccurate. The inaccuracy usually becomes more problematic as the sensors are positioned further away from the object being tracked, and if there are other disturbances interrupting the tracking signals.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments will now be described by way of example only with reference to the appended drawings wherein:

FIG. 1 a schematic diagram of a tracking engine tracking the position and angular orientation of one or more objects.

FIG. 2 is a block diagram of an example configuration of a tracking engine and tracking unit.

FIG. 3 is a block diagram of example data components in the tracking unit's memory.

FIG. 4 is a schematic diagram of example data components in the tracking engine's state machine.

FIG. 5 is a block diagram of one configuration of a configurable real-time environment tracking and command module (RTM) connected to various devices, including a tracking engine, for tracking or controlling physical objects.

FIG. 6 is a schematic diagram illustrating one example of the generation of a virtual environment from a physical environment using the RTM.

FIG. 7 is a flow diagram illustrating example computer executable instructions for tracking an object from the perspective of the tracking engine.

FIG. 8 is a flow diagram illustrating example computer executable instructions for providing tracking data from the perspective of the tracking unit.

FIG. 9 is a flow diagram illustrating further example computer executable instructions for tracking an object from the perspective of the tracking engine.

FIG. 10 is a flow diagram illustrating example computer executable instructions for associating an object ID with the position of a light source using acceleration information.

FIG. 11 is a flow diagram illustrating example computer executable instructions for associating an object ID with the position of a light source using strobe pattern information.

FIG. 12 is a flow diagram illustrating example computer executable instructions for distinguishing and tracking beacon light sources from other non-tracking light sources based on a strobe pattern.

FIG. 13 is a flow diagram illustrating example computer executable instructions for tracking and identifying an object from the perspective of the tracking engine using acceleration information.

FIG. 14 is a flow diagram illustrating example computer executable instructions for tracking and identifying an object from the perspective of the tracking engine using strobe pattern information.

FIG. 15 is a flow diagram illustrating example computer executable instructions for tracking an object when only one camera or none of the cameras are able to view a light source of the tracking unit, from the perspective of the tracking engine.

FIG. 16 is a flow diagram illustrating example computer executable instructions for selecting beacon modes for the tracking unit from the perspective of the tracking engine and the tracking unit.

FIG. 17 is a schematic diagram illustrating example data components of the tracking unit and tracking engine.

DETAILED DESCRIPTION

It will be appreciated that for simplicity and clarity of illustration, where considered appropriate, reference numerals may be repeated among the figures to indicate corresponding or analogous elements. In addition, numerous specific details are set forth in order to provide a thorough understanding of the embodiments described herein. However, it will be understood by those of ordinary skill in the art that the embodiments described herein may be practiced without these specific details. In other instances, well-known methods, procedures and components have not been described in detail so as not to obscure the embodiments described herein. Also, the description is not to be considered as limiting the scope of the embodiments described herein.

In the field of tracking systems, it is known to use image tracking as one approach. However, it has been recognized that image tracking can become increasingly ineffective without proper lighting conditions. Further, image tracking is limited in its tracking range. Objects further away from a camera cannot be easily tracked. Moreover, the proposed systems and methods desire to track multiple objects simultaneously in real time. However, known image tracking systems have difficulty achieving such tracking performance for various reasons (e.g. complexity of object recognition and objects blocked from sight).

Other known tracking systems relate to inertial measurements, such as measuring changes in angular orientation and position over time. However, such systems are considered to be less accurate than image tracking techniques that are capable of providing absolute positioning.

In general, systems and methods are provided for tracking an object (e.g. position and angular orientation). The system includes a computing device in communication with at least two cameras, each of the cameras able to capture images of one or more light sources attached to an object of the one or more objects. The one or more light sources are associated with an object ID able to be determined from the images. The system also includes a receiver in communication with the computing device, whereby the receiver is able to receive at least angular orientation data and the object ID associated with the object. The computing device determines the object's position by comparing the images of the one or more light sources and generates an output comprising the position, the angular orientation data, and the object ID of the object.

In another aspect, each of the cameras are able to capture images of a single light source attached to the object. In another aspect, each of the one or more light sources comprise an infrared light emitting diode and the cameras are sensitive to infrared light. In another aspect, the receiver is also able to receive inertial acceleration data associated with the object. In another aspect, the angular orientation data comprises roll, pitch, and yaw and the position comprises X, Y, and Z coordinates. In another aspect, the angular orientation data is measured by one or more gyroscopes attached to the object. In another aspect, the inertial acceleration data is measured by one or more accelerometers attached to the object. In another aspect, a given light source of the one or more light sources is associated with the object ID able to be determined from the images by: the receiver also receiving inertial acceleration data associated with the object; the computing device determining an acceleration and a position of the given light source by comparing a series of images of the given light source captured by the cameras; and, upon determining that the acceleration determined from the series of images is approximately equal to the received inertial acceleration data, the computing device associating the received object ID with the given light source's position. In another aspect, a given light source of the one or more light sources is associated with the object ID able to be determined from the images by: the computing device detecting from a series of images a strobe pattern associated with the given light source; and, upon determining that the detected strobe pattern matches a known strobe pattern having a known object ID, the computing device associating the known object ID with the given light source's position. In another aspect, upon associating the given light source's position with the object ID, the computing device determines if a subsequent position of the given light source in subsequent images is within an expected vicinity of the given light source's position, and if so, associating the object ID with the subsequent position. In another aspect, the computing device only compares the images of the one or more light sources that have a strobe pattern. In another aspect, the system further comprises a transmitter, wherein the computing device is able to send a beacon mode selection via the transmitter to control when the one or more lights are displayed and when the angular orientation data is received. In another aspect, the computing device comprises a state machine that uses a Kalman filter or an extended Kalman filter to generate the output comprising the position and the angular orientation of the object. In another aspect, upon the computing device detecting that only one of the cameras is able to detect the light source, or none of the cameras are able to detect the light source: the computing device identifying a last known position of the object as determined from the images; and, the computing device determining a new position of the object by combining the inertial acceleration data with the last known position.

A tracking apparatus that is able to be attached to an object is also provided. The tracking apparatus includes one or more infrared light sources; an inertial measurement unit able to measure at least roll, pitch and yaw; a wireless radio for transmitting at least measurements obtained from the inertial measurement unit and an associated object ID; and, wherein the one or more infrared light sources are able to be detected by at least two cameras and the measurements are able to be transmitted to a computing device that is in communication with the cameras.

In another aspect, the one or more infrared light sources is a single infrared LED. In another aspect, the inertial measurement unit is able to measure acceleration along the X, Y and Z axes. In another aspect, the tracking apparatus further comprises a battery for powering the tracking apparatus. In another aspect, the tracking apparatus further comprises a processor, wherein the processor controls the single infrared light emitting diode with a strobe pattern. In another aspect, the strobe pattern is associated with the object ID. In another aspect, the tracking apparatus further comprises a memory for storing one or more beacon modes, the one or more beacon modes determining at least one of: which one or more types of measurements obtained from the inertial measurement unit are to be transmitted; a time period that the single infrared LED is active; and a time period that measurements obtained from the inertial measurement unit are transmitted to the computing device. In another aspect, the tracking apparatus further comprises a belt, wherein the belt is able to be wrapped around the object.

A kit of components or parts is also provided for tracking one or more objects. The kit includes a tracking apparatus able to be attached to an object, the tracking apparatus including: one or more light sources that strobe according to a strobe pattern associated with an object ID; an inertial measurement unit; a wireless radio for transmitting at least measurements obtained from the inertial measurement unit and the object ID. The kit of components or parts also includes at least two cameras, each of the cameras able to capture images of the one or more light sources. The kit further includes a receiver configured to receive from the tracking apparatus at least the measurements and the object ID. The kit further includes a computing device able to obtain at least the images from the at least two cameras and the measurements and the object ID from the receiver, the computing device configured to at least: analyze the images to determine the strobe pattern of the one or more light sources; identify the object ID based on the strobe pattern; determine a position of the one or more light sources by at least determining a pixel location of the one or more light sources in the images; and after confirming the object ID identified using the strobe pattern and the object ID obtained via the receiver are the same, generating an output comprising the object ID, the position and the measurements. In general, the components or parts described in this document may be assembled or integrated, or both, to form the systems and perform the methods also described in this document.

Turning to FIG. 1, a schematic diagram of a tracking engine 106 for outputting position and angular orientation data of one or more objects is provided. Objects or people 102 can be tracked by having attached to them a tracking unit 104. Each object has a tracking unit 104 which is able to measure at least angular orientation data and able to activate one or more light sources 126. Two or more cameras 100 are used to track the position of the light sources 126. The camera images of the light sources 126 are sent to the tracking engine 106 for processing to determine the absolute position of the object 102. The measured angular orientation data is transmitted, preferably, although not necessarily, wirelessly, to the tracking engine 106, for example through the receiver 108. Preferably, the tracking unit 104 is wireless to allow the objects 102 to move around freely, unhindered. The tracking engine then combines the position data and angular orientation data to generate a six-degrees-of-freedom output (e.g. X, Y, Z coordinates and roll, pitch, yaw angles).

The light source 126 can be considered a passive reflective marker, a heating element, an LED, a light bulb, etc. The light from the light source 126 may not necessarily be visible to the human eye. An active light source is preferred to allow the cameras to more easily track the light source. It has also been recognized that light sources visible to the human eye can be distracting. Furthermore, visible light sources can also be washed out or overpowered by other light, such as by spot lights, which make the light source 126 difficult to track using the camera images. Therefore, it is preferred, although not required, that the light source 126 be an infrared light source, such an infrared LED, since its light energy is more easily detected amongst the other types of lights being used. Further, infrared sensitive cameras can be used to detect only infrared light, thereby increasing the accuracy of tracking a light source. It can therefore be appreciated that an infrared LED and use of infrared sensitive cameras reduces the effects of various (e.g. bright or low-level) light conditions, and reduces visual distractions to others who may be seeing the tracking unit 104. The active infrared LEDs can also be viewed at very far distances.

As shown in FIG. 1, some objects may have a single light source 126, while other objects may have multiple light sources 126. It can be appreciated that at least one light source 126 is sufficient to provide image tracking data, although multiple light sources 126 can increase the image tracking of an object from various angles. For example, if a person 102 is being tracked by cameras 100, the light sources 126 can be more easily seen by the cameras 100 if the light sources are placed on different parts of the person 102 (e.g. the head, the back, and the front of the person). In this way, as the person turns or moves around, although one light source 126 is occluded from the cameras, another light source 126 remains visible to the cameras 100.

In another embodiment, a single light source 126 that is associated with an object is preferred in some instances because it is simpler to track from an image processing perspective. By only processing an image of a single light source that corresponds to an object, the response time for tracking the object can be much faster. The benefits are compounded when attempting to track many different objects, and each single light source that is imaged can be used to represent the number of objects. The single light source sufficiently provides the positional data, while allowing the tracking engine 106 to very quickly process the locations of many objects. Moreover, using a single light source 126 in each tracking unit 104 conserves power, and thus the length or period of operation of the tracking unit 104.

It can also be appreciated that two or more cameras are used to provide tracking in three dimensions. Using known optical tracking methods, the cameras' 2D images of the light source 126 are used to triangulate a 3D position (e.g. X, Y, Z coordinate) for the light source 126. Although two cameras are sufficient for determining the position, more than two cameras (e.g. three cameras) can provide more accurate data and can track an object from more angles.

Further, each of the light sources 126 can be pulsed at certain speeds or at certain strobe patterns. The pulsing or strobe pattern can be used to distinguish a visual tracking signal of a tracking unit 104 from other lights sources (e.g. stage lighting, car lights, decorative lights, cell-phone lights, etc.) that are within the vicinity of the tracking unit 104. In this way, the other non-tracking light sources are not mistakenly perceived to be the tracking light sources 126. The light sources 126 can also be pulsed at different speeds or at different strobe patterns relative to other tracking light sources 126, in order to uniquely identify each object. For example, a first light source 126 can pulse at a first strobe pattern, while a second light source 126 can pulse at a second strobe pattern. The first and second light sources 126 can be uniquely identified based on the different strobe patterns. In other words, many different objects can be individually tracked and identified using few cameras.

It can therefore be seen that the combination of the image tracking and inertial tracking accurately provides six degrees of freedom at very high response rates. Further, the objects can be tracked from far distances. Additionally, multiple objects can be tracked by simply attaching a tracking unit 104 onto each object that is to be tracked.

Turning to FIG. 2, an example configuration of a tracking unit 104 and a tracking engine 106 are shown. The tracking unit 104 includes a processor 124, one or more infrared LEDs 126, an inertial measurement unit (IMU) 130, a radio 132, a timer 135, memory 128 and a battery 134. It is noted that an infrared LED 126 is one of many different types of light sources 126 that can be used herein, and thus, reference numeral 126 is used interchangeably with the infrared LED and with light sources in general. Although a battery 134 is shown, it can be appreciated that the tracking unit 104 can be powered through alternate known means, such as power chords. Further, although a radio 132 is shown, it can be appreciated that other wired or wireless communication devices can be used with the tracking unit 104. It can be appreciated that the packaging or assembly of the tracking unit or tracking apparatus 104 can vary. For example, the one or more LEDs 126 may be located on one part of the object and the IMU 130 may be located on another part of the object. In another example, the LED 126 could be attached to the object by plugging in the LED 126 into the object, and connecting the LED 126 to the processor 124 through wired or wireless communication. In an example embodiment, multiple LEDs 126 are affixed to different locations on an object or a person, and the LEDs are connected by wire or wirelessly to a housing that houses the processor, the memory, the IMU, etc. The tracking unit or tracking apparatus 104 can be attached to an object using a belt, fastener, adhesive, clip, weld, bolts, etc. In another embodiment, more than one tracking unit 104 can be attached to an object. For example, when tracking different body parts on a person, one tracking unit 104 can be placed on an arm, another tracking unit 104 can be placed on the person's waist, and another tracking unit 104 can be placed on a leg. It can therefore be appreciated that the tracking unit 104 can be attached to an object in various ways.

In an example embodiment, multiple light sources 126 are attached or are part of the same tracking apparatus 104. Each light source 126 strobes or has a blinking pattern that conveys the same object identification (ID), but also has a blinking pattern that differs from the other light source(s) of the same tracking apparatus. In this way, each light source is uniquely identified from another light source of the same tracking apparatus, while still being able to identify that the different light sources are associated with the same object ID. For example, each light has a unique strobe pattern, although more than one strobe pattern may be associated with the same object ID. In another example, different light sources associated with the same tracking apparatus (e.g. same object ID) operate using the same strobe pattern.

The battery 134 can be rechargeable and is used to power the components of the tracking unit 104. The IMU 130 may comprise three axis gyroscopes and three axis accelerometers for measuring angular orientation and inertial acceleration, respectively. The angular orientation information and inertial acceleration measured from the IMU 130 is wirelessly transmitted through the radio 132 to the tracking engine 106. As described above, other data communication methods and devices are also applicable. The processor 124 also associates with the IMU data an object identification. The object identification can be stored in memory 128. A separate timer 135 is provided, or the processor 124 also acts as a timer, to record a time stamp at which the inertial measurement data is measured or obtained. As will be discussed below, the time stamp may be used to help correlate the measurement data from the IMU 130 with other data obtained from the cameras. The timer 135 (or as implemented by the processor 124) is synchronized with the timer 133 of the tracking engine.

As discussed earlier, tracking units 104 can be associated with a strobe pattern or blinking pattern. Therefore, the memory 128 can store the strobe pattern for the infrared LED 126 and the associated object identification. The processor 124 retrieves the object identification and wirelessly transmits the object identification with the IMU measurements and, optionally, the associated time stamps associated with each of the IMU measurements; this data is received by the receiver and transmitter 108 at the tracking engine 106. The processor 124 also retrieves the strobe pattern associated with the object identification and controls the flashing of the infrared LED 126 according to the strobe pattern. The processor 124 also has the ability to send commands, for example, through the radio 132, to activate operations in other control devices. Although not shown, in an embodiment using wireless communication, the antennae of the receiver and transmitter 108 can be physically attached to the cameras 100 in order to create a wireless mesh allowing the tracking engine 106 to more easily communicate with the one or more tracking units 104. In other words, each camera 100 can attached an antenna of the receiver and transmitter 108. The wireless communication can, for example, use the Zigby protocol.

Turning briefly to FIG. 3, an example of data components are shown in the tracking unit's memory 128. The memory 128 includes an object ID 310, a strobe pattern 312, and IMU data 314. Any data, such as IMU data 314, that is transmitted from the tracking unit 104 to the tracking engine 106 is accompanied by the object ID 310. In this way, the tracking engine 106 can correlate the tracking unit data with an object ID 310. As described above, the strobe pattern 312 is also associated with the object ID 310. In some cases the strobe pattern 310 is unique from other strobe patterns to uniquely identify the object ID 310. The memory 128 also includes beacon modes 302, which determine the manner in which the tracking unit 104 gathers and transmits data to the tracking engine 106. Example beacon modes include “always active” 302, “sometimes active” 306 and “active for given periods” 308. In mode 304, the tracking unit 104 always activates the one or more light sources 126 and always transmits angular orientation data, acceleration data, etc. In mode 306, the tracking unit 104 sometimes activates the one or more light sources 126, and sometimes transmits the IMU data. In mode 308, the one or more light sources 126 are active for only certain or predetermined periods of time and the IMU data is transmitted at the same times. Other beacon modes 302 (not shown) may include activating the one or more light sources 126 but not the IMU 130, or vice versa. It can be appreciated that the beacon modes 302 may be selected using controls, such as buttons or switches, (not shown) on the tracking unit. In addition, or in the alternative, the beacon modes 302 may be selected by the tracking engine 106. The tracking engine 106 can send commands to the tracking unit 104 to select different beacon modes 302. It can be appreciated that selecting different beacon modes 128 can help manage the processing of data by the tracking engine 106. For example, objects that are considered important can have attached tracking units 104 that are in an “always active” beacon mode 304. Objects considered less important can have attached tracking units 104 that are in a “sometimes active” beacon mode 306. In this way, less data is obtained and processed by the tracking engine 106, thereby reducing the tracking engine's processing load.

Although not shown, the tracking unit 104 can include other devices, such as magnetometers and gravity sensors, to measure other attributes.

Turning back to FIG. 2, the light from the infrared LED 126 is detected by two or more cameras 100. The cameras 100 are preferably able to acquire images at a high rate and are connected to the tracking engine 106 in a way to increase data transfer. For example, the cameras can gather images at 240 frames per second and are connected in a star configuration. The cameras may also be Ethernet gray scale cameras that provide a resolution of 0.8 megapixels. The camera images are sent to the tracking engine 106.

In an example embodiment, a time stamp for each image is generated by the tracking engine 106 (e.g. via the timer 133). In particular, the timer 133 keeps the time and sends time information to each camera. In return, each camera uses the time information to mark each image with the received time information, and sends the image and time information to the tracking engine. It will be appreciated that the timers of each of the one more tracking units, the cameras and the tracking engine are synchronized.

In another example embodiment, such as in the alternative or in addition, each of the cameras 100 also have a timer that is synchronized with the timer of the tracking unit 104, and the timer of each camera records a time stamp of each image that is captured.

The tracking engine 106 can be a computing device or series of computing devices operating together, herein collectively referred to as a computing device. The tracking engine 106 includes: a camera motion capture module 112 for identifying the one or more light sources and associated data (e.g. position, acceleration, heading, strobe patterns, time stamps, etc.); an object identification module 114 for identifying objects and associated data; a data prioritizing module 120 for prioritizing the processing and transfer of data; a timer 133 for keeping time and synchronizing time between the devices; and a state machine 300 for collecting different data measurements and calculating the current state (e.g. position and angular orientation) of one or more objects.

The timer 133 may interact with one or more of the modules in the tracking engine and may interact with the cameras and the tracking apparatus. In an example embodiment, the timer operates as a counter and increments (e.g. counts up) once every set interval of time. In an example embodiment, the counter increments every 10 milliseconds, or, in other words, the counter increments upwards 100 times a second. The timer 133 is initialized at zero, along with any other timers (e.g. of the one or more tracking apparatuses and as well as, optionally, other devices). The value of the timer 133 counts upwards, one integer at a time (e.g. 0, 1, 2, 3, etc.). The value of the timer is herein also called a time stamp.

The timer 133 sends this time data to each camera. In an example embodiment, the time information used to mark each image is the same as the frame number of each camera. For example, if the timer 133 and each camera operate at the same rate, such that the timer increments at the same rate as each camera captures an image, then the time information (e.g. time stamp) is used to mark the frame number. As a further example, the timer 133 may increment at a rate of 100 times per second (e.g. frequency of 100 Hz) and each camera 100 operates at 100 frames per second (e.g. captures 100 frames per second). In this example, if the cameras 100 and the timer 133 operate at the same rate, when the timer has a value ‘1’, a camera's frame ID or number is ‘1’; when the timer has a value ‘2’, another camera's frame ID or number is ‘2’; when the timer has a value of ‘3’, yet another camera's frame ID or number is ‘3’; and so forth.

In an example embodiment, the timer 133 sends a synchronization signal to the cameras more often than it sends a synchronization signal to each tracking apparatus. The time synchronization signal includes the current time value of the tracking engine's timer 133, which is used by other devices to ensure they have the same time value. In an example embodiment, the timer 133 sends a time synchronization signal to each camera at the same rate of the frames-per-second. In an example embodiment, the timer 133 sends a time synchronization signal, comprising the value of the timer 133, every 10 milliseconds (e.g. 100 times a second), and the tracking engine receives an image from each camera every 10 milliseconds (e.g. 100 frames per second). In an example embodiment, the timer 133 sends a time synchronization signal, comprising the value of the timer 133, once every second (e.g. at a frequency of 1 Hz). It will be appreciated that other frequencies and frame-per-second values can be used other than the values explicitly described herein.

It is appreciated that the time synchronization signal is preferably, though not necessarily, sent more often to the cameras compared to the tracking apparatuses since the positioning data obtained from the cameras is more time-sensitive and a higher-degree of position accuracy may be obtained from the images. The time synchronization data may be sent less frequently to each tracking apparatus, compared to the cameras, since the IMU data measured by each tracking apparatus may not be as time-sensitive.

In an example embodiment, the tacking apparatus transmits yaw, pitch and roll, as well as X,Y,Z coordinates or acceleration data along such axes. The position coordinates or acceleration data may be redundant in view of the image data and later only used in the case of obfuscation (absence of the pulsing lights or strobe lights in the images). The data from the tracking apparatus is sent over a wireless transmission that may be packaged to match the data inflow from the cameras (e.g. along a CAT6 cable or other wire) to the tracking engine.

Continuing with FIG. 2, the camera motion capture module 112 receives the images and, optionally the frame IDs (e.g. where each frame ID is also used as a time stamp), from the cameras 100 and determines the three dimensional position of each infrared LED 126. Known imaging and optical tracking techniques can be used. It will be appreciated, however, that the proposed systems and methods described herein are able to track and identify many objects based on the imaging data, and such systems and methods can be combined with imaging techniques.

The camera motion capture module 112 is also able to detect strobe patterns of the LEDs. In one embodiment, the camera motion capture module 112 uses the strobe patterns to differentiate light sources 126 for tracking from other light sources (e.g. car lights, decorative lights, cell phone lights, etc.) that are not used for tracking. In other words, only light sources 126 having a strobe pattern are tracked for their position.

It will be appreciated that the time stamps (e.g. or frame ID) associated with the images and the time stamps associated with the IMU data, where the data sets have the same object ID, can be used to temporally align the image data and the IMU data. In other words, it can be determined which data from the different data sets occurred at the same time.

The camera motion capture module 112 can also extract data for identifying objects. In one approach for identifying an object, the camera motion capture module 112 determines the current position of an infrared LED 126 and sends the current position to the object identification module 114. The object identification module 114 compares the current position with previous positions that are associated with known object IDs. If a current position and a previous position are sufficiently close to one another, taking into account the time elapsed between the position measurements, then the current position of the infrared LED 126 is associated with the same object ID corresponding to the previous position. The object identification module 114 then returns the position and object ID to the camera motion module 112. In another approach, the camera motion capture module 112 determines the acceleration and heading of a given infrared LED 126 and this information is sent to the object identification module 114. The object identification module 114 also receives from a tracking unit 104 acceleration data, an associated object ID and optionally a time stamp associated with inertial data (e.g. acceleration data). The object identification module 114 then compares the acceleration determined from the camera motion capture module 112 with the acceleration sent by the tracking unit 104. If the acceleration and headings are approximately the same, for example within some allowed error value, then the location of the given infrared LED is associated with the same object ID corresponding to the acceleration data from the tracking unit 104. The object identification module 114 then returns the position of the infrared LED 126 and the associated object ID to the camera motion capture module 112. In another approach for identifying objects associated with the infrared LEDs 126, as described above, the camera motion capture module 112 is able to detect strobe patterns. In addition to using strobe patterns to distinguish non-tracking lights from tracking lights, the strobe patterns can also be used to identify one object from another object. For example, the position and strobe pattern of a certain LED is sent to the object identification module 114. The object identification module 114 holds a database (not shown) of object IDs and their corresponding strobe patterns. The module 114 is able to receive object IDs and strobe patterns from the tracking units 104, via the receiver 108. The object identification module 114 receives the position and strobe pattern from the camera motion capture module 112 and identifies the corresponding object ID based on matching the imaged strobe pattern with known strobe patterns in the database. When a match is found, the position and object ID are sent back to the camera motion capture module 112. In another example approach, the object identification module is able to compare data obtained from the tracking unit 104 with data obtained from the camera motion capture module 112 when the data from the unit 104 and the module 112 have the same time stamp information. If the different data has the same time stamp information and is similar to each other (e.g. acceleration, velocity, etc. is generally the same based on images and the inertial measurements), then the data is associated with the same object.

The above approaches for tracking and identifying multiple tracking units 104 and objects can be combined in various ways, or used in alternative to one another. It can be appreciated that the object identification module 114 can also directly output the positions of the infrared LEDs 126 to the state machine 300.

As mentioned earlier, the object ID, angular orientation and inertial acceleration data can be sent by a tracking unit 104 and received by the receiver 108. Preferably, the object ID is included with IMU data, whereby the object ID is associated with the IMU data.

The state machine 300 receives the position and associated object ID from the camera motion module 112 or the object identification module 114. The state machine 300 also receives the IMU data (e.g. acceleration, angular orientation, true north heading, etc.) from the receiver 108. In an example embodiment, time stamps associated with the IMU data and the position information from the camera motion module 112, as well as the object IDs, are used to associate the information with each other (e.g. based on matching time stamps and matching object IDs). The state machine 300 uses these measurements to update the state models. In one example, the state machine 300 uses a particle filter to update the state models. Examples of such particle filters include the Kalman filter and extended Kalman filter, which are known algorithms for estimating a system's varying quantities (e.g. its position and angular orientation state) using control inputs and measurements. In the proposed systems and methods, the measurement data is gathered from the cameras 100 and IMU 130.

An example of data components in the state machine 300 is shown in FIG. 4. Associated with each object ID 316 is a previous state 318, measurement data 320, and a current state 322. The current state 322 is determined by the measurement data 320 and the previous state 318. Upon determining the current state 322, the current state 322 becomes the previous state 318 in order to calculate the next current state 322. In other words, the current state 322 is updated in a recursive manner.

By way of background, noisy sensor data, approximations in the equations that describe how a system changes, and external factors that are not accounted for introduce some uncertainty about the inferred values for a system's state. When using the Kalman filter, the state machine 300 averages a prediction of a system's state with a new measurement using a weighted average. The purpose of the weights is that values with better (i.e., smaller) estimated uncertainty are “trusted” more. The weights are calculated from the covariance, a measure of the estimated uncertainty of the prediction of the system's state. The result of the weighted average is a new state estimate that lies in between the predicted and measured state, and has a better estimated uncertainty than either alone. This process is repeated every step, with the new estimate and its covariance informing the prediction used in the following iteration. This means that the Kalman filter works recursively and requires only the last “best guess”—not the entire history—of a system's state to calculate a new state. When performing the actual calculations for the filter, the state estimate and covariances are coded into matrices to handle the multiple dimensions involved in a single set of calculations. This allows for representation of linear relationships between different state variables (such as position, velocity, and acceleration) in any of the transition models or covariances.

Particle filters, such as Kalman filters and extended Kalman filters, are able to update a state (e.g. the position and angular orientation) at any time upon receiving measurements. In other words, the receipt of the position measurements and the angular orientation measurements do not need to be synchronized, and the measurements can be received by the state machine 300 in any order. For example, the state machine 300 can receive position data more often than angular orientation data for a particular object, and the state of that particular object will be updated as the new measurements are received. This allows for the state machine 300 to update the objects' states at the fastest speed possible, even if IMU 130 has a slower data-gathering rate compared to the camera motion capture module 112. The particle filters are also versatile as they are able to update the state of an object using different types of data. For example, although the camera motion capture module 112 may not be able to provide position data at times because the light sources 126 are occluded or blocked from the cameras' view, the state machine 300 can receive acceleration data from the tracking unit 104 through the receiver 108. Based on the last known position or state of the object and the acceleration information, the state machine 300 can calculate the new position. In this way, various types of data can be used to generate an updated state (e.g. position and angular orientation).

It will be appreciated that other types of particle filtering algorithms can be used. More generally, algorithms used for updating an object's state (e.g. position and angular orientation) using measurements are applicable to the principles described herein.

In an example embodiment, the state machine 300 or another module, such as smoothing module (not shown), also further processes the information from the state machine to smooth the data. If a smoothing module is used, the smoothing module receives and processes the outputted data from the state machine 300. In particular, the state machine or the smoothing module identifies that the movement of an object may be erratic, though accurate, and may apply a smoothing function to the data outputted by the state machine. Generally, the aim of smoothing is to give a general idea of relatively slow changes of the position and/or angular orientation data with little attention paid to the close matching of data values. Non-limiting examples of smoothing algorithms and filters include: additive smoothing, Butterworth filter, Kalman filter, Kernel smoother, smoothing spline, exponential smoothing, moving average, linear least squares, and Savitzky-Golay filter. The smoothing is used, for example), to filter out or dampen the smaller erratic movements of an object.

Consider, for example, a person walking that, with each step, moves up and down. This is also called “bounce” when a person walks. When the person is wearing a tracking unit and their movement is being tracked, it is sometimes desirable to capture the person's movement but also eliminate the bounce to theft step. In another example, a person may be standing in one position, but may sway side-to-side or shift. Again, while it is desirable to track the position of the person, it is also desirable to eliminate the tracking of the person's swaying or shifting. This is useful, for example, when a robotic camera or a lighting system, or a light projector, is using the position and angular orientation data from the tracking engine to follow a person; the camera or follow light should not bounce up and down or sway side-to-side. Using the smoothing capability of the state machine or the smoothing module, the erratic position and orientation data is mitigated and a smoother set of data is outputted by the tracking engine.

Turning back to FIG. 2, the output of information from the tracking engine 106 can be very fast, for example at 50 Hz or more. In another example, the tracking engine operates at 100 Hz (e.g. when processing and outputting data). Other operating speeds or frequencies can be used. In an example embodiment, the tracking engine operation speed matches the speed at which the timer keeps track of time. For example, if the response rate or operate speed is 100 Hz, then the timer 133 increments time at the same frequency. The data response rate can, for example, be maintained by prioritizing the data. For example, the data prioritizing module 120 can prioritize the gathering of positional data over the angular orientation data, so that the positional data is accurate all the time, while the angular orientation data may be updated although with some delay. Additionally, to conserve computing resources, when computing the position when light sources 126 are occluded, the processing of camera images can be delayed. In particular, when using the inertial positioning data, the camera images are not relied upon to determine the position of the LED and, thus, there is no need to process the camera images as quickly.

As described earlier, the data processing speed can further be increased by managing the data flow tracking units 104. The data prioritizing module 120 in the tracking engine 106 can send commands to the tracking units 104 to select different beacon modes 302. By commanding certain of the tracking units 104 to transmit data less frequently (e.g. “sometimes active” mode 306), there will be less data to process. This allows the tracking engine's computing resources to be used to more quickly process the data (e.g. camera images of light sources 126, IMU data, etc.) of those tracking units 104 that output data all time (e.g. “always active” mode 304).

In another aspect of the system shown in FIG. 2, the time stamp information is used to determine which data is most up-to-date or recent. In particular, the data with the most recent time stamp information is used to determine the position and orientation of an object, while older data may be discarded. Alternatively, the older data is used to determine direction, acceleration, velocity, angular acceleration, angular velocity, etc. Ignoring the older data or using only the most up-to-date data facilitates the tracking engine to focus processing resources on the most recent data to help achieve faster performance, and performance that is considered real-time.

The time stamp information may also be used, for example, to provide error checks. In one example, the tracking engine determines if the series of received data are associated with time stamps that progress or advance compared to previous time stamps. If the time stamps do not advance or progress, then the received data is considered to be erroneous. In another example of error checking, the acceleration data (or derived velocity or position data thereof) obtained from the IMU and the position data (or the derived velocity or acceleration data thereof) obtained from the images can be compared for the same instance in time. If the data does not match for the same instance in time, then the received data is considered erroneous.

In another aspect of the system, it is appreciated that multiple tracking units 104 may communicate with the tracking engine 106. If a high number of tracking units are communicating with the tracking engine at the same time, then the bandwidth of the wireless data network may be saturated and/or the performance of the receiver 108 may degrade. Data may be lost or the speed that data is transmitted may slow down. To address such issues, the radio 132 in one or more tracking units may be shut down for periods of time. This reduces the amount of data being transmitted to the tracking engine and saves battery power of a tracking unit 104. In another example, the radio 132 may remain on, but it does not transmit. It will be appreciated that while the radio may be shut down, the tracking unit is still active by activating the LED(s) 126. The command to shut off the radio or to stop transmitting data over the radio may originate from the tracking unit processor 124 or from the tracking engine 124. For example, the tracking engine may have contextual information that certain tracking units are not critical for tracking at certain periods of time and, thus, those certain tracking units are commanded to shut off their radios or to stop transmitting for those certain time periods.

In another example embodiment of the system, the tracking engine 106 sends a reset command at time intervals to the one or more tracking units. The reset command instructs each tracking unit to re-initialize their sensors and, possibly other processes. For example, the IMU 130 is reset or re-initialized to a reset value. The reset value may be stored in memory 128 of the tracking unit, or may be generated by the tracking engine and sent to a given tracking engine as part of the reset command. For example, based on the image data processed by the tracking engine, the tracking engine computes a reset value for the IMU, so that the IMU generates measurements that are consistent with the image data. The reset command may be sent out every Y number of seconds or minutes or hours (e.g. or combinations thereof). In another example, the reset command is broadcasted based on external triggers.

In an example embodiment, the reset command is generated and used under other conditions including one or more of: a cold boot, when the system is turned on; a hot boot, when the system is operational and then reset manually or automatically; and when the time value of the timer 133 reaches a maximum value. In another example, each time a record of the reset enables calculation of the actual time (e.g. in hours, minutes, seconds, etc.).

In another example embodiment, the reset command includes resetting the timer 133 and the timer 135 (e.g. to ‘0’).

In another example aspect of the tracking engine 106, the tracking engine is able to determine if one or more cameras is misaligned or outputting erroneous image data. It is herein recognized that it is difficult to determine if the camera is misaligned based on looking at a camera or monitoring the orientation of the camera. Furthermore, it is difficult to determine that the image data is erroneous since, the image data may, on its face, be correct, but is no longer compliant with the initial calibration setting. In particular, to determine if one or more cameras is misaligned or outputting erroneous image data, the tracking engine receives images from multiple cameras. If the majority of cameras produce images that correlate with each other to provide an agreed upon location of a light source, but one camera (or a minority of cameras) provides image data that does not correspond with the image data from the majority of cameras, then the one camera (or the minority of cameras) is determined to be malfunctioning. For example, in a four-camera system, three cameras confirm that a light source is located at a given location, but a fourth camera provides image data that indicate the same light source is located at a different location. The fourth camera is then determined to be misaligned or malfunctioning. When such a determination is made, the tracking engine may generate a recalibration command to recalibrate the fourth camera or may generate a command to recalibrate all the cameras. The recalibration may occur automatically. In another example, upon making such a determination, the tracking engine generates an alert for a technician to realign or recalibrate the fourth camera. In another example, upon making such a determination, image data provided by the offending camera (e.g. the fourth camera) is ignored by the tracking engine and the image processing relies on image data from the remaining cameras.

It can be appreciated that the tracking engine 106 outputs both position (e.g. X, Y, Z coordinates) and angular orientation (e.g. roll, pitch, yaw) information associated with an object, or an object ID where there are many objects being simultaneously tracked. The tracking engine may also output a time stamp associated with the data, although not required. Such information is valuable in tracking objects and can be used by other systems. For example, in the security industry or the live entertainment industry, it is desirable to track the position and orientation of hundreds of people simultaneously. The tracking systems and methods described herein can be used to accomplish such tracking. The tracking information outputted by the tracking engine 104 may also be visualized on other computing systems. An example of such a computing system is a real-time tracking module, available under the name BlackBox™ by CAST Group of Companies Inc. Details of a real-time tracking module are provided in U.S. application Ser. No. 12/421,343, having Publication No. 2010/0073363 to Gilray Densham et al., the contents of which are herein incorporated by reference in its entirety.

Turning to FIG. 5, an example configuration of a real-time tracking module (RTM) 24 is shown, whereby the RTM 24 coordinates multiple clients for tracking, visualizing and controlling objects in a three dimensional environment. The various clients connected to the RTM 24 are able to communicate via the RTM 24, either directly or indirectly. Thus, the RTM 24 facilitates the coordination of the clients and enables the clients to interoperate, even when provided by different vendors. In this example, the clients include the tracking engine 106, which provides tracking data of one or more objects in six degrees of freedom. Other clients include a general control console 30, general sensor console 32, motion console 34, media server 36, lighting console 38, safety proximity system 42, 3D audio position system 44, lighting designer's remote 46, robotic arm 48, helicopter control console 50, stage manger's remote 52, and robotic camera 54. The stage manager's remote 52, for example, sends commands to the RTM 24 to control the virtual objects in the virtual environment 4, thereby controlling the media server 36, lighting console 38 and helicopter control console 50. There may also be a local positioning system (LPS) 56 to track a helicopter 23 a. It can be appreciated that a LPS 56 refers to any device or combination of devices that can determine the location of an object within a localized environment. Examples of devices used in an LPS 56 include RADAR, SONAR, RFID tracking and cameras. The tracking engine 106 is an example of an LPS 56. Such devices are able to measure or sense various characteristics of the physical environment. It can be appreciated that the number and type of clients connected to the RTM 24 as shown in FIG. 5 is non exhaustive. Further, the RTM 24 is configurable to interact with various numbers and types of clients by providing a common, recognizable interface that the client trusts and will enable to interoperate with other clients that it may not otherwise trust.

The interfacing between a client and the RTM 24 is based on predetermined software protocols that facilitate the exchange of computer executable instructions. In other words, a client sends and receives data and computer executable instructions using a file format that is understood by both the client and the RTM 24. Examples of such a file format or protocol include dynamic link libraries (DLL), resource DLLs and .OCX libraries. Thus, a client having a file format which is recognized by the RTM 24 may interface with the RTM 24. Once the software interfacing has been established, clients can interact with the RTM 24 in a plug and play manner, whereby the RTM 24 can discover a newly connected client, or hardware component, with little or no device configuration or with little additional user intervention. Thus, the exchange of data between the client and RTM 24 begins automatically after plugging the client into the RTM 24 through the common interface. It can be appreciated that many types of clients are configurable to output and receive a common file format and thus, many types of clients may advantageously interact with the RTM 24. This flexibility in interfacing reduces the integration time as well as increases the number of the RTM's applications. Also, as noted above, this provides the RTM 24 as a trusted intermediate platform for interoperating multiple client types from multiple vendors.

In an example embodiment, a tracking unit 104 can be placed on a helicopter in order to provide feedback on the helicopter's positional coordinates, as well as roll, pitch and yaw. This information is outputted from the tracking engine 106 to the RTM 24, and then sent to the helicopter control console 50. In another example, the tracking unit 104 can be attached or worn by an actor. The actor's position can be tracked and provided to the RTM 24, which interacts with the safety proximity system 42. If the safety proximity system 42, based on the positional data from the tracking engine 106, detects that the actor is moving into a dangerous area, then a safety alert can be generated or a safety action can be initiated.

It can therefore be seen that the tracking engine 106 and tracking unit 104 can be used with a RTM 24.

More generally, the tracking engine 106 is configured to communicate with one or more clients either directly or via the RTM 24. Clients may be fixed or may have movement capabilities. A client that interacts with the tracking engine or uses data outputted by the tracking engine may also be called an automated downstream client or device. Other non-limiting examples of clients include a media projector and a display monitor (e.g. a television screen).

In an example embodiment, if the location or position output of a tracking unit is to be useful to downstream clients (e.g. an automated downstream device), then such downstream clients are also calibrated to the already-calibrated Origin position (e.g. X,Y,Z coordinates (0,0,0)) of the cameras 100. Ongoing consolidated calibrated concurrence means the clients will rely on target coordinates which are precise (on target) if upstream devices (e.g. cameras 100) and downstream devices or clients are calibrated to concur on the same Origin. In an example embodiment, the cameras 100 are calibrated to Origin (e.g. 0,0,0) and the downstream clients are subsequently calibrated to use the same 0,0,0. In this way, the downstream clients are able to process the data outputted by the tracking engine in a meaningful way relative to the real-world surroundings, and thus achieve precise targeting.

In an example embodiment, the position data from the tracking engine is used by a client, such as a media projector (e.g. also called projector) or a display monitor, to determine what type of images should be shown, where the images are to be shown, when the images should be shown, or combinations thereof. For example, when a media projector displays images on a surface, the media projector, or a control device for the projector, needs to know exactly where the images are to be displayed (e.g. the pixel locations of the projector should be mapped to the location of the surface). One or more tracking units 104 may be used to identify the location of the surface and to determine which pixel positions of the media projector are calibrated or mapped to align with the surface's location. For example, in the process of calibration, the tracking units 104 determine the real-world location of the surface and are also used to determine a mapping between pixel locations relative to real-world locations. For example, one or more test points (e.g. light spots) are projected by the media projector based on known pixel locations and the real-world position of the one or more test points is determined by using a tracking unit; this creates a mapping between pixel locations and real-world locations. The multiple test points may be used to generate a mapping matrix which maps operational parameters of an automated downstream client with real-world locations. The calibration process may also be used to calibrate other automated downstream clients (e.g. audio systems, laser grids, display devices, camera devices, etc.). It will be appreciated that the implementation of test points varies by the automated downstream client. For example, a test point for an audio system is the projection of sound emanating from a specific location, such as the location of the source of the sound (e.g. the mouth of a person), or is a projection to where the sound is to be targeted. In another example, a test point of a camera is a location (e.g. pixel location, depth of field, front focal length, etc.) of the camera's focus point.

In another example embodiment, the tracking engine may also be used to calibrate upstream cameras 100, where one or more tracking units 104 may be used to reference real-world locations. A specific type of tracking unit 104, called a wand, with active or passive light sources is provided for calibrating the cameras 100. The light sources on the wand are positioned at fixed relative locations from each other.

Turning to FIG. 6, further details of the RTM 24 and the use of the tracking engine 106 are provided. A system diagram shows objects in a physical environment 2, in this case a stage, mapping onto a virtual environment 4. It can be appreciated that the virtual environment 4 resides within a computing environment, for example, having various processors, memory, interfaces, computer readable media, etc. Moreover, the virtual environment 4 can also be part of the RTM 24. A memory storage or database 22 of virtual objects and attributes is provided to correspond with the physical objects in the physical environment 2. For clarity, references to physical objects include the suffix ‘a’ and references to virtual objects include the suffix ‘b’. The physical environment 2 in FIG. 6 comprises a first platform 18 a supported below by a second platform 20 a. An overhead truss 6 a extends across the platforms 18 a, 20 a and is supported at its ends by two vertical supports 8 a, 10 a. A robotic light 12 a is supported on the truss 6 a for illuminating the first platform 18 a, whereupon a first person 14 a and a second person 16 a are positioned. A wirelessly controlled helicopter drone 23 a is flying above the platforms 18 a, 20 a. Although not shown, the helicopter drone 23 a, the first person 14 a, and the second person 16 a may each be equipped with their own tracking unit 104. A three-dimensional origin or physical reference point 7 a is positioned in front of the platforms 18 a, 20 a, whereby the positions of the physical objects are measured relative to the physical reference point 7 a.

Each of these physical objects in the physical environment 2 are mapped onto the virtual environment 22, such that the virtual environment database 22 organizes the corresponding virtual objects and any corresponding attributes. The physical reference point 7 a is mapped into the virtual environment 22, thus forming a virtual origin or reference point 7 b. The positions and angular orientations of the virtual objects are mapped relative to the virtual reference point 7 b. In this example, the virtual objects comprise a virtual helicopter 23 b, a first virtual platform 18 b, a second virtual platform 20 b, a first vertical support 8 b, a second vertical support 10 b, a virtual truss 6 b, a virtual robotic light 12 b, a first virtual person 14 b, and a second virtual person 16 b. Physical attributes corresponding to each physical objects are also represented as virtual attributes corresponding to each virtual object, wherein attributes typically include the position, angular orientation, and dimensions of the objects as well as any data related to movement of the objects (e.g. speed, rotational speed, acceleration, etc.). In one embodiment, the position may be represented in Cartesian coordinates, such as the X, Y and Z coordinates. Other attributes that may also be used to characterize a virtual object include the rotor speed for the helicopter 23 a, the maximum loads on the truss 6 a, the angular orientations (e.g. roll, pitch, yaw) and the weight of a person 14 b. The position and angular orientation of the helicopter 23 a and the persons 14 a, 16 a, are tracked by their respective tracking units 104 and the tracking engine 106. This information is reflected or updated in the virtual environment 4.

It can be appreciated that accurately depicting the virtual environment 4 to correspond to the physical environment 2 can provide a better understanding of the physical environment, thereby assisting the coordination of the clients within the physical environment. The process of depicting attributes of a physical object onto a corresponding virtual object can be considered a physical-to-virtual mapping. Accurately depicting the virtual environment 4 may comprise generating virtual objects based on data automatically provided by clients connected to the RTM 24. Alternatively, some of the virtual objects and their corresponding attributes may be manually entered into the virtual environment database 22. For example, an operator or technician of the RTM 24 may gather the dimensions of a truss and determine its center of mass and volumetric center. The operator may then create a virtual object with the same dimensions, center of mass and volumetric center that corresponds to the truss. The physical location of the truss, with respect to the physical reference point 7 a, is also used to characterize the location of the virtual object. Thus, the virtual object corresponds very closely to the truss in the physical environment.

Other methods of generating a virtual environment 4 that accurately represent a physical environment include the use of three-dimensional computer drawings, floor plans and photographs. Three-dimensional computer drawings or CAD drawings, using many standard file formats such as .dwg, WYG, Viv, and .dxf file formats, can be uploaded through a conversion system, such as BBX, into the RTM's virtual environment 22. The computer drawings of the virtual objects are scaled to match the dimensions of the physical objects; this mapping process does advantageously reduce the time to generate a virtual environment 4. Additionally, floor plans may be used to generate virtual objects. For example, a floor plan of a house showing the location of the walls may be scanned into digital form in the computer. Then, the walls in the virtual environment are given a height that corresponds to the height of the physical walls. Photographs, including 3D photographs, may also be used to create a virtual environment as they typically illustrate relative dimensions and positions of objects in the physical environment regardless of the scale. An operator may use the photograph to generate a three-dimensional computer drawing or generate a virtual object directly by specifying the dimensions of the object. Photographs may also be used to generate a three-dimensional model using semi or fully automated 3D reconstruction algorithms by measuring the shading from a single photograph, or from a set of point correspondences from multiple photographs.

It can also be appreciated that the location of the physical reference point 7 a can be positioned in any location. Preferably, the location of the physical reference point 7 a is selected in a fixed, open area that facilitates consistent and clear measurement of the locations of physical objects relative to the physical reference point 7 a. As can be seen from FIG. 6, the physical reference point 7 a is located at the coordinates (0,0,0) in the physical environment. Similarly, the virtual reference point 7 b is mapped in the same position as the physical reference point 7 a and is located at the coordinates (0,0,0) in the virtual environment. It can be appreciated that accurate correlation between the reference points 7 a, 7 b can be used to calibrate and verify the correspondence between the physical and virtual environments.

Continuing with FIG. 6, a visualization engine 26 uses the information stored in the virtual environment database 22 to generate a graphic, thereby illustrating or visualizing the physical environment 2 to permit interaction with a user. In other words, the visualization engine 26 provides a graphic of the virtual environment 4, which in turn substantially corresponds to the physical environment 2. In the example configuration according to FIG. 6, the visualization engine 26 is part of the virtual environment 4, although not necessarily.

It can therefore be seen that a tracking engine 106 and tracking unit 104 can be used with a RTM 24 to track a person or moving object and display the visualization of the same based on the updated position and angular orientation data in a visualization engine 26.

It will be appreciated that any module or component exemplified herein that executes instructions or operations may include or otherwise have access to computer readable media such as storage media, computer storage media, or data storage devices (removable and/or non-removable) such as, for example, magnetic disks, optical disks, or tape. Computer storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data, except transitory propagating signals per se. Examples of computer storage media include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by an application, module, or both. Any such computer storage media may be part of the tracking engine 106 or tracking unit 104 or accessible or connectable thereto. Any application or module herein described may be implemented using computer readable/executable instructions or operations that may be stored or otherwise held by such computer readable media.

Turning to FIG. 7, example computer executable instructions are provided for tracking one or more objects from the perspective of the tracking engine 106. At block 136, at least two cameras capture images of one or more light sources attached to an object of the one or more objects, whereby the one or more light sources are each associated with an object ID able to be determined from the images. At block 138, images of a given light source are compared to determine the three-dimensional position of the light source 126 (e.g. using stereoscopic imagery or triangulation techniques). At block 140, the tracking engine 106 receives at least the angular orientation data and object ID associated with the object. At block 142, an output is generated combining the position, the angular orientation, and the object ID of the object.

FIG. 8 provides example computer executable instructions for tracking an object from the perspective of a tracking unit 104. At block 144, a single infrared LED that is attached to the object is activated. In other instances, multiple other types of light sources can be attached to the same object. At block 146, the tracking unit 104 measures at least roll, pitch and yaw on the same object using an IMU. At block 148, the measurements from the IMU and an associated object ID are wirelessly transmitted to a computing device (e.g. the tracking engine 106), wherein the computing device is in communication with at least two cameras that are able to detect the single infrared LED.

Turning to FIG. 9, example computer executable instructions are provided for tracking an object, from the perspective of the tracking engine 106. At block 170, at least two cameras 100 capture an initial set of images of one or more light sources 126, the light sources 126 attached to an object or objects. At block 172, the tracking engine 106 initializes object identification tagging to associate one or more of the light sources with an object ID. Tagging one or more of the light sources with an object ID will be explained below with respect to FIGS. 10 and 11. It can be appreciated that the position of the light sources are also identified when determining the object IDs. Upon associating the light source or light sources with object IDs, the cameras capture subsequent images of the one or more light sources 174. In the subsequent images, a pixel location of each of the one or more light sources is identified. The pixel locations are then compared with a frame of reference to determine the current X,Y,Z coordinates of the one or more light sources.

A frame-to-frame identification approach is then used to determine the object IDs associated with the current coordinates of the light sources. It can be appreciated that methods for tracking objects in video sequences or in consecutive image frames are known. Examples of such frame-to-frame tracking include feature extraction and feature detection. An example of feature extraction or feature detection is “blob detection”, which is used to define points of interest that are tracked from frame to frame. At block 178, the current coordinates of the one or more light sources are compared with the previous coordinates (and optionally, headings) of the light sources that have been associated with object IDs. In other words, the positions of known objects are compared with positions of unknown objects. At block 180, it is determined if the objects IDs of the current coordinates can be determined through the comparisons. Such a determination is made by, for example, by determining if the current coordinates (without an object ID) are close enough to the previous coordinates (with an object ID). If not, then the object ID of the current coordinates cannot be determined. Then, at block 192, object identification tagging is applied to associated the current coordinates with an object ID. The approaches for object identification tagging are described with respect to FIGS. 10 and 11.

Continuing with FIG. 9, if at block 180 the object ID can be determined for the current coordinates, at block 184, based on the comparisons, the current coordinates are associated with an object ID. For example, at block 198, based on the previous position and heading of a first object, it is determined if the current coordinates of a light source are within the expected vicinity of the previous position. If so, the first object's ID is associated with the current coordinates.

At block 186, the state machine 300 receives the current coordinates and associated object ID. The state model corresponding to the object ID is then updated with the X, Y, Z coordinates. At block 188, the tracking engine 106 also receives the angular orientation data and object ID associated with the object being tracked. The inertial acceleration data and any other data sent by the tracking unit 104 may also be received. The state model corresponding to the object ID is then updated with the angular orientation data or inertial acceleration data, or both. At block 192, an output is generated comprising the object ID and the associated X, Y, Z coordinates and angular orientation. The process then repeats, as represented by dotted line 196, by returning to block 174. Subsequent images of one or more light sources are captured and used to identify a current location of the object.

At block 194, at certain times (e.g. periodic times, under certain conditions and instances), the object identification tagging of the light sources is re-initialized to associate one or more of the light sources with an object ID. For example, every 5 seconds, instead of using frame-to-frame image tracking, object identification tagging is used.

Turning to FIGS. 10 and 11, two approaches for object identification tagging are provided. These approaches can be used in combination with the frame-to-frame image tracking, as described above in FIG. 9. FIG. 10 provides computer executable instructions for tracking an object by comparing the visually computed acceleration vector of a light source with the acceleration data (and associated object ID) sent by a tracking unit 104. FIG. 11 provides computer executable instructions for tracking an object by detecting the strobe pattern of a light source and comparing the strobe pattern with a database correlating object IDs and strobe patterns. Either one of the approaches in FIGS. 10 and 11, or both, can be used with the method in FIG. 9.

Turning to FIG. 10, at block 330, based on consecutive images from at least two cameras, the tracking engine 106 determines the X, Y, Z coordinates and acceleration vector of a given light source or light sources associated with an object. At block 332, the tracking engine 106 also receives inertial acceleration data and an object ID, both associated with the same object. At block 334, it is determined whether or not the received inertial acceleration data approximately equals to the acceleration vector of the give light source. For example, if it is detected using the consecutive camera images that a light source is accelerating at 1 m/s² along the Y axis and the received inertial acceleration data, having a known object ID, measures that the tracking unit 104 is accelerating at 1.01 m/s² along the Y axis, then the X,Y,Z coordinates of the light source are associated or tagged with the known object ID. However, at block 334, if the acceleration vector from the camera images and the inertial acceleration data from the IMU 130 do not match within a given error tolerance, then it is determined if the received inertial acceleration data is approximately equal to the acceleration vector of another light source. The data-comparison process repeats at block 334 to continue identifying other lights sources.

Turning to FIG. 11, at block 340, based on consecutive images from at least two cameras, the X,Y,Z coordinates are determined and a strobe pattern or strobe patterns are detected, whereby both the coordinates and the one or more strobe patterns are associated with one or more light sources. It can be appreciated that multiple light sources that are part of the same tracking unit 104 can have the same strobe pattern. At block 342, an object ID is identified based on a given strobe pattern (e.g. by comparing the strobe pattern with a database of strobe patterns corresponding to object IDs). When a match between the detected strobe pattern and a strobe pattern in the database is found, then the corresponding object ID in the database is associated with the one or more light sources. At block 344, the X, Y, Z coordinates are associated with the identified object ID, as they both correspond to a same strobe pattern.

Turning to FIG. 12, example computer executable instructions are provided for capturing images of the one or more light sources (e.g. blocks 170 and 174 shown in FIG. 9). As described above, the light sources 126 used as a tracking beacon can be more easily distinguished from non-tracking light sources when the light sources 126 are pulsing. In particular, at block 346, the tracking engine 106 captures a set or series of images (such as, for example, consecutive images) of one or more light sources from at least two cameras. At block 348, based on the set of consecutive images, the tracking engine 106 determines which of the light sources strobe on and off. At block 350, the tracking engine 106 marks the light sources 126 that strobe as beacon or tracking light sources. The tracking engine 106 ignores the other light sources and does not determine their locations. In an example embodiment, the tracking engine ignores light sources that do not strobe in order to avoid or reduce processing resources used to determine the information about such light sources (e.g. does not identify strobe pattern or location, or both). At block 352, the tracking engine 106 identifies a pixel location for only the marked beacon or tracking light sources. The tracking engine 106 then proceeds to determine the X, Y, Z coordinates or the object ID, or both for the marked beacon or tracking light sources. It will be appreciated that the computer executable instructions described in FIG. 12 can be combined with other systems and methods of tracking described herein.

In another embodiment, the tracking engine 106 can determine the position coordinates and object ID of a light source 126 by comparing acceleration data and need not use frame-to-frame image tracking as described above. Turning to FIG. 13, example computer executable instructions are provided for tracking and identifying objects by comparing acceleration data determined from camera images and from an IMU 130. At block 354, at least two cameras capture images of one or more light sources, each light source attached to an object. At block 356, the pixel locations of at least one of the light sources in the images is identified, and the pixel locations are compared with a frame of reference to determine the X, Y, Z coordinates and acceleration vector of the at least one light source. At block 358, the angular orientation, inertial acceleration data and object ID associated with the object is received by the tracking engine 106. At block 360, it is determined whether or not the received inertial acceleration data is approximately equal to the acceleration vector of a given light source. If not, then at block 362 it is determined if the received inertial acceleration data is approximately equal to the acceleration vector of another light source, in order to identify a matching object ID. However, if, at block 360, it is determined that the received inertial acceleration data from the IMU 130 does approximately equal the acceleration vector determined from the camera images, then at block 364, the X, Y, Z coordinates of the given light source are associated with the received object ID. At block 366, the state model corresponding to the object ID is updated with the X, Y, Z coordinates. At block 368, the state model corresponding to the object ID is also updated with the angular orientation data or inertial acceleration data, or both. At block 370, the tracking engine 106 generates an output comprising the object ID and the associated X, Y, Z coordinates and angular orientation. At block 372, the position and angular orientation data corresponding to the object ID is saved, for example in the state machine 300.

In another embodiment, the tracking engine 106 is able to track and identify an object or many objects simultaneously using the strobe patterns. The tracking engine 106 in this embodiment does not use frame-to-frame image tracking as described above. Turning to FIG. 14, example computer executable instructions are provided for tracking and identifying an object by comparing strobe patterns with other strobe patterns having associated object IDs. At block 374, at least two cameras capture images of one or more light sources, each light source attached to an object. At block 376, a pixel location of the one or more light sources in the images is identified, and the tracking engine 106 compares the pixel location with a frame of reference to determine the X, Y, Z coordinates of the one or more light sources. At block 378, a strobe pattern is detected from the images of the one or more light sources. At block 380, an object ID is identified based on the detected strobe pattern. For example, the detected strobe pattern is compared with a database of strobe patterns having corresponding object IDs. When a match of strobe patterns is found, the corresponding object ID from the database is also associated with the detected strobe pattern and the coordinates of the strobe light (block 382). At block 384, at least angular orientation data and object ID, and optionally inertial acceleration data, are received by the tracking engine 106. At block 386, the received data (e.g. from the tracking unit 104) is associated with the X, Y, Z coordinates based on comparing and matching the object IDs. The measurements (e.g. coordinates, angular orientation, acceleration, etc.) are used to update the state model corresponding to the object ID. At block 388, an output is generated comprising the object ID and associated X, Y, Z coordinates and angular orientation. At block 390, this data (e.g. current state) is saved in association with the object ID, for example, in the state machine 300.

It can therefore be seen that in the above approaches, hundreds of different objects can be simultaneously tracked based on the using acceleration data, different or unique strobe patterns, frame-to-frame image tracking, and combinations thereof.

Turning to FIG. 15, example computer executable instructions are provided for tracking an object, and in particular, switching between tracking approaches under certain conditions. The instructions are provided from the perspective of the tracking engine 106. At block 392, the tracking engine 106 tracks the position of a light source, which can be associated with an object ID, using camera images. At block 394, the tracking engine 106 detects that only one camera can view the single light source, or that none of the cameras are able to view the single light source. In other words, the light source 126 is occluded in a way that an insufficient number of cameras are able to view the light source 126 to obtain a 3D coordinate. At block 396, the last known position of the occluded single light source is retrieved. The last known position can be determined from the images or from an iteration using the inertial acceleration data. At block 398, the tracking engine 106 wirelessly receives the angular orientation data, the inertial acceleration data and the object ID associated with the object. At block 404, the receipt of the inertial acceleration data is prioritized over the comparison of images, thereby allowing critical operations to be processed more quickly. At block 402, based on the matching object IDs, the last known position and the inertial acceleration data are used to determine a new position (e.g. new X, Y, Z coordinates) of the object. At block 404, the new X, Y, Z coordinates are associated with the angular orientation data and the inertial acceleration data based on comparing and matching object IDs. At block 406, an output comprising the object ID, associated X, Y, Z coordinates and angular orientation data is generated. At block 408, upon detecting that the light source associated with the object ID is viewable again by at least two cameras (e.g. no longer occluded), the tracking engine 106 determines the X, Y, Z coordinates using the camera images. The priority of the operations is also updated, whereby the comparison of camera images is given a higher priority over the receipt and processing of angular orientation data.

FIG. 16 shows example computer executable instructions between a tracking engine 106 and a tracking unit 104. In some situations, for the benefit of conserving energy and increasing response speed, the inertial acceleration data is only provided by the tracking unit 104 upon the request of the tracking engine 106. As described above, the tracking unit 104 can provide data according to certain beacon modes (e.g. “always active”, “sometimes active”, “active for given periods of time”, etc.). Some of the beacon modes can also include providing certain data, such as just the angular orientation data, or providing both angular orientation data and inertial acceleration data. The beacon modes can be determined by receiving a selection command from the tracking engine 104. At block 450, the tracking engine 106 sends a beacon mode selection to the tracking unit 104, such as to measure and return both angular orientation data and inertial acceleration data. Meanwhile, the tracking unit 104, controls a single infrared LED or multiple light sources with a strobe pattern, whereby the strobe pattern is associated with an object ID. At block 462, the tracking unity 104 measures roll, pitch and yaw and the inertial acceleration in the X, Y, Z axes on the same object using the IMU 130. At block 464, the tracking unit 104 receives from the tracking engine 106 the beacon mode selection for both angular orientation and acceleration data. The tracking unit 104, upon detecting that there is a request for inertial data (block 466), transmits both the angular orientation data, the inertial acceleration data, and the associated object ID to the computing device (e.g. the tracking engine 104). It can be appreciated that the computing device is in communication with at least two cameras able to detect the single infrared LED 126. If the acceleration data is not requested, as per the beacon mode, then only the angular orientation data and the associated object ID are sent to the computing device (e.g. the tracking engine 104) (block 470).

Meanwhile, the tracking engine 106 tracks the position of the light source using camera images (block 452). The tracking engine 106 detects that only one or none of the cameras are no longer able to view the single light sources (block 454). For example, the single light source is occluded from all the cameras, or occluded from all the cameras but one. The last known position of the occluded single light source is retrieved (block 456). Then at block 458, the tracking engine 104 receives the angular orientation data, inertial acceleration data and the object ID associated with the object. The tracking engine 106 can then continue to execute operations set out in blocks 400, 402, 404, 406, and 408, as per FIG. 15.

In one embodiment, the inertial acceleration data is measured at all times. In another embodiment, the inertial acceleration data is measured only in certain beacon modes as selected by the tracking engine 106; this saves energy and increases processing efficiency for both the tracking unit 104 and the tracking engine 106.

Turning to FIG. 17, example data components associated with the tracking units 104 a and 104 b and the tracking engine 106 are shown. In particular, a first tracking unit 104 a includes object ID1 (224), strobe pattern 1 (226), angular orientation 1 (234) and inertial acceleration) (236). The IMU measurement data can be, although not necessarily, stored in the tracking unit 104 a. Similarly, the second tracking unit 104 b is associated with its own object ID 2 (22), strobe pattern 2 (230), angular orientation 2 (238) and inertial orientation 2 (240). The measurement data from both the first tracking unit 104 a and the second tracking unit 104 b, as well as the object IDs (224 and 228) are sent to the tracking engine 106.

The tracking engine 106 includes a database 232 for storing and associating the object ID 208, the strobe pattern 210, the position data 212, the angular orientation data 214 and the inertial acceleration data 216. This information is organized according to the object IDs. This information, as described above, is also stored in a state model associated with the object ID. The information extracted or outputted from the database 232 includes the object ID 218, as well as the associated position 220 and angular orientation 222.

It can be appreciated that the above systems and methods can be applied to, for example, tracking objects, animals or people, or for any moving or static item whereby its position and its direction of movement are desired to be known. The systems and methods can be used for tracking in lighting, audio, and entertainment marketplaces, military, security, medical applications, scientific research, child care supervision, sports, etc.

The schematics and block diagrams used herein are just for example. Different configurations and names of components can be used. For instance, components and modules can be added, deleted, modified, or arranged with differing connections without departing from the spirit of the invention or inventions.

The steps or operations in the flow charts and diagrams described herein are just for example. There may be many variations to these steps or operations without departing from the spirit of the invention or inventions. For instance, the steps may be performed in a differing order, or steps may be added, deleted, or modified.

It will be appreciated that the particular embodiments shown in the figures and described above are for illustrative purposes only and many other variations can be used according to the principles described. Although the above has been described with reference to certain specific embodiments, various modifications thereof will be apparent to those skilled in the art as outlined in the appended claims. 

1. A kit of components for tracking one or more objects, comprising: a tracking apparatus able to be attached to an object, the tracking apparatus comprising: one or more light sources that strobe according to a strobe pattern associated with an object ID; an inertial measurement unit; a wireless radio for transmitting at least measurements obtained from the inertial measurement unit and the object ID; at least two cameras, each of the cameras able to capture images of the one or more light sources; a receiver configured to receive from the tracking apparatus at least the measurements and the object ID; and a computing device able to obtain at least the images from the at least two cameras and the measurements and the object ID from the receiver, the computing device configured to at least: analyze the images to determine the strobe pattern of the one or more light sources; identify the object ID based on the strobe pattern; determine a position of the one or more light sources by at least determining a pixel location of the one or more light sources in the images; and after confirming the object ID identified using the strobe pattern and the object ID obtained via the receiver are the same, generating an output comprising the object ID, the position and the measurements.
 2. The kit of components of claim 1 wherein the one or more light sources are infrared light sources and the cameras are sensitive to infrared light.
 3. The kit of components of claim 1 wherein the computing device further comprises a state machine calculator for computing a current state of the object, the current state comprising the position and angular orientation of the object.
 4. The kit of components of claim 1 wherein the inertial measurement unit measures at least inertial acceleration.
 5. The kit of components of claim 1 wherein the inertial measurement unit measures at least angular orientation, which comprises roll, pitch, and yaw.
 6. The kit of components of claim 1 wherein the computing device is further configured to ignore a light source that does not strobe when analyzing the images.
 7. The kit of components of claim 1 wherein the measurements include inertial acceleration data, and the computing device is further configured to: determine an acceleration and a position of a given light source of the one or more light sources by comparing a series of images of the given light source; and, after determining that the acceleration determined from the series of images is approximately equal to the inertial acceleration data, the computing device associating the object ID obtained from the receiver with the position of the given light source.
 8. The kit of components of 7 wherein the computing device is further configured to: after associating the position of the given light source with the object ID, determine if a subsequent position of the given light source in subsequent images is within an expected vicinity of the position of the given light source, and if so, associating the object ID with the subsequent position.
 9. The kit of components of claim 1 wherein the computing device is further configured to: determine if a subsequent position of the one or more light sources in subsequent images is within an expected vicinity of the position, and if so, associating the object ID with the subsequent position.
 10. The kit of components of claim 1 further comprising a transmitter, wherein the computing device is able to send a beacon mode selection to the tracking apparatus via the transmitter to control when the one or more light sources are active.
 11. The kit of components of claim 1 wherein the measurements comprise inertial acceleration data, and the computing device is further configured to: detect that only one of the cameras is able to detect the one or more light sources, or none of the cameras are able to detect the one or more light sources; identify a last known position of the object as determined from the images; and determining a new position of the object by combining the inertial acceleration data with the last known position.
 12. The kit of components of claim 1 wherein the tracking apparatus further comprises a memory for storing one or more beacon modes, the one or more beacon modes determining at least one of: which one or more types of measurements obtained from the inertial measurement unit are to be transmitted; a time period that the one or more light sources is active; and a time period that measurements obtained from the inertial measurement unit are transmitted by the wireless radio.
 13. A system for tracking one or more objects, comprising: a tracking apparatus able to be attached to an object, the tracking apparatus comprising: one or more light sources that strobe according to a strobe pattern associated with an object ID; an inertial measurement unit; a wireless radio for transmitting at least measurements obtained from the inertial measurement unit and the object ID; at least two cameras, each of the cameras able to capture images of the one or more light sources; a receiver configured to receive from the tracking apparatus at least the measurements and the object ID; and a computing device able to obtain at least the images from the at least two cameras and the measurements and the object ID from the receiver, the computing device configured to at least: analyze the images to determine the strobe pattern of the one or more light sources; identify the object ID based on the strobe pattern; determine a position of the one or more light sources by at least determining a pixel location of the one or more light sources in the images; and after confirming the object ID identified using the strobe pattern and the object ID obtained via the receiver are the same, generating an output comprising the object ID, the position and the measurements.
 14. The system of claim 13 wherein the computing device further comprises a state machine calculator for computing a current state of the object, the current state comprising the position and angular orientation of the object.
 15. The system of claim 13 wherein the inertial measurement unit measures at least inertial acceleration.
 16. The system of claim 13 wherein the inertial measurement unit measures at least angular orientation, which comprises roll, pitch, and yaw.
 17. The system of claim 13 wherein the computing device is further configured to ignore a light source that does not strobe when analyzing the images.
 18. The system of claim 13 wherein the measurements include inertial acceleration data, and the computing device is further configured to: determine an acceleration and a position of a given light source of the one or more light sources by comparing a series of images of the given light source; and, after determining that the acceleration determined from the series of images is approximately equal to the inertial acceleration data, the computing device associating the object ID obtained from the receiver with the position of the given light source.
 19. The system of claim 13 wherein the computing device is further configured to: determine if a subsequent position of the one or more light sources in subsequent images is within an expected vicinity of the position, and if so, associating the object ID with the subsequent position.
 20. The system of claim 13 wherein the measurements comprise inertial acceleration data, and the computing device is further configured to: detect that only one of the cameras is able to detect the one or more light sources, or none of the cameras are able to detect the one or more light sources; identify a last known position of the object as determined from the images; and determining a new position of the object by combining the inertial acceleration data with the last known position. 